package net.bwie.realtime.jtp.dwd.log.job;

import com.alibaba.fastjson.JSON;
import net.bwie.realtime.jtp.dwd.log.function.AdjustIsNewProcessFunction;
import net.bwie.realtime.jtp.utils.KafkaUtil;
import net.bwie.realtime.jtp.dwd.log.function.AppLogSplitProcessFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SideOutputDataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;

/**
 * dwd层数据应用开发：将ods层采集原始日志数据，进行分类处理，存储kafka队列
 *  数据流向：kafka->flink datastream->kafka
 */
public class JtpLogEtlJob {
    public static void main(String[] args) throws Exception {
        //1.执行环境-env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //2.数据源-source
        DataStream<String> kafkaDateStream = KafkaUtil.consumerKafka(env, "topic-log");
//        kafkaDateStream.print("kafka");

        //3.数据转换-transformation
        DataStream<String> pageStream=processLog(kafkaDateStream);

        //4.数据输出-sink
        KafkaUtil.producerKafka(pageStream, "dwd-traffic-page-log");


        //5.执行-execute
        env.execute("JtpLogEtlJob");

    }

    /**
     * 对实时获取APP流量日志数据进行ETL处理，并且进行分流，存储Kafka消息队列
     *      1-数据清洗
     *      2-新老访客状态标记修复
     *      3-数据分流
     * @param stream app日志流
     */
    private static DataStream<String> processLog(DataStream<String> stream) {
        //1-数据清洗
        DataStream<String> jsonStream=appLogCleaned(stream);

        //2-新老访客状态标记修复
        DataStream<String> etlStream=processIsNew(jsonStream);


        //3-数据分流
        DataStream<String> pageStream=splitStream(jsonStream);

        //返回数据流
        return pageStream;
    }

    /**
     *
     * 3-日志数据分流
     */
    private static DataStream<String> splitStream(DataStream<String> stream) {
        // todo 第一步：侧边流输出标记
        final OutputTag<String> errorTag = new OutputTag<String>("error-log"){};
        final OutputTag<String> startTag = new OutputTag<String>("start-log"){};
        final OutputTag<String> displayTag = new OutputTag<String>("display-log"){};
        final OutputTag<String> actionTag = new OutputTag<String>("action-log"){};

        //todo 第二步：日志分流处理
        SingleOutputStreamOperator<String> pageStream=stream.process
                (new AppLogSplitProcessFunction(errorTag, startTag, displayTag, actionTag));

        //todo 第三步：测流输出
        SideOutputDataStream<String> errorStream = pageStream.getSideOutput(errorTag);
        KafkaUtil.producerKafka(errorStream, "dwd-traffic-error-log");
        SideOutputDataStream<String> startStream = pageStream.getSideOutput(startTag);
        KafkaUtil.producerKafka(startStream, "dwd-traffic-start-log");
        SideOutputDataStream<String> displayStream = pageStream.getSideOutput(displayTag);
        KafkaUtil.producerKafka(displayStream, "dwd-traffic-display-log");
        SideOutputDataStream<String> actionStream = pageStream.getSideOutput(actionTag);
        KafkaUtil.producerKafka(actionStream, "dwd-traffic-action-log");

        //todo 第四步：输出主流
        return pageStream;
    }

    private static DataStream<String> processIsNew(DataStream<String> stream) {
        //a-按照设备ID进行分组
        KeyedStream<String, String> midStream = stream.keyBy(new KeySelector<String, String>() {
            @Override
            public String getKey(String value) throws Exception {
                return JSON.parseObject(value).getJSONObject("common").getString("mid");
            }
        });


        //b-状态编程，对is_new校验修复
        DataStream<String> isNewStream=midStream.process(new AdjustIsNewProcessFunction());

        //c-返回数据流
        return isNewStream;
    }

    /**
     * 1-数据清洗，将不合格数据侧边输出
     * @param stream 原始app流量日志数据流
     * @ return 数据流
     */
    private static DataStream<String> appLogCleaned(DataStream<String> stream) {
        //a-脏数据侧边流输出时标签
        final OutputTag<String> dirtyTag = new OutputTag<String>("dirty-log"){};

        //b-数据清洗处理
        SingleOutputStreamOperator<String> cleanedStream = stream.process(new ProcessFunction<String, String>() {

            @Override
            public void processElement(String value, Context ctx, Collector<String> out) throws Exception {
                try {
                    //a.解析json数据
                    JSON.parseObject(value);
                    //b.没有异常，解析正确，正常输出
                    out.collect( value);
                }catch (Exception e){
                    //c.捕获异常，侧边流输出数据
                    ctx.output(dirtyTag, value);
                }
            }
        });

        //c.侧边流输出：脏数据
        SideOutputDataStream<String> dirtyStream = cleanedStream.getSideOutput(dirtyTag);
        KafkaUtil.producerKafka(dirtyStream, "dwd-traffic-dirty-log");

        //d-返回正常数据流
        return cleanedStream;
    }
}
